Laboratory of Protein Design & Immunoengineering

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The binding and catalytic functions of proteins are generally mediated by a small number of functional residues held in place by the overall protein structure. Here, we describe deep learning approaches for scaffolding such functional sites without needing ...

De novo protein design enables the exploration of novel sequences and structures absent from the natural protein universe. De novo design also stands as a stringent test for our understanding of the underlying physical principles of protein folding and may ...

Knowledge-based approaches use the statistics collected from protein data-bank structures to estimate effective interaction potentials between amino acid pairs. Empirical relations are typically employed that are based on the crucial choice of a reference ...

Inferring protein-protein interactions from sequences is an important task in computational biology. Recent methods based on Direct Coupling Analysis (DCA) or Mutual Information (MI) allow to find interaction partners among paralogs of two protein families ...

Protein Engineering, especially protein post-translational modification (PTM), ex-tends proteomes in a more complex way than one can expect from analysis of their encod-ing genomes. It can activate or deactivate certain catalytic functions, add new desired ...

Recent advances in protein-design methodology have led to a dramatic increase in reliability and scale. With these advances, dozens and even thousands of designed proteins are automatically generated and screened. Nevertheless, the success rate, particular ...

Despite the structural and functional information contained in the statistical coupling between pairs of residues in a protein, coevolution associated with function is often obscured by artifactual signals such as genetic drift, which shapes a protein's ph ...

Proteins are fundamental components in biological systems and crucial for a variety of biological functions. Over the last decades, significant progress in structural biology has facilitated the study of proteins and their interactions with molecular partn ...

BackgroundStatistical potentials, also named knowledge-based potentials, are scoring functions derived from empirical data that can be used to evaluate the quality of protein folds and protein-protein interaction (PPI) structures. In previous works we deco ...

Direct-coupling analysis (DCA) for studying the coevolution of residues in proteins has been widely used to predict the three-dimensional structure of a protein from its sequence. We present RADI/raDIMod, a variation of the original DCA algorithm that grou ...

Imaging of proteins at the single-molecule level can reveal conformational variability, which is essential for the understanding of biomolecules. To this end, a biologically relevant state of the sample must be retained during both sample preparation and i ...

Laboratory of Protein Design & Immunoengineering

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Scaffolding protein functional sites using deep learning

A generic framework for hierarchical de novo protein design

Statistical potentials from the Gaussian scaling behaviour of chain fragments buried within protein globules

Correlations from structure and phylogeny combine constructively in the inference of protein partners from sequences

Protein Engineering for Personalized therapy and diagnosis

Assessing and enhancing foldability in designed proteins

Extracting phylogenetic dimensions of coevolution reveals hidden functional signals

On the exploration of novel methodological approaches for immunogen design: case studies in influenza and hepatitis C

SPServer: split-statistical potentials for the analysis of protein structures and protein-protein interactions

On the use of direct-coupling analysis with a reduced alphabet of amino acids combined with super-secondary structure motifs for protein fold prediction

Low-energy electron holography imaging of conformational variability of single-antibody molecules from electrospray ion beam deposition